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Hillock is a lightweight, three-tier memory engine for AI agents that runs entirely on your local hardware — no cloud APIs, no telemetry, no GPU cluster required. It replaces bloated vector databases with a relational SQLite Knowledge Graph, Hebbian synaptic memory, and Hyperdimensional Computing (HDC), extracting structured facts from your documents and blocking unanswerable questions mathematically before they ever reach the LLM. Built for AI engineers, local LLM developers, and privacy-focused researchers who need deterministic, auditable memory on consumer hardware.

Quick Start

Install Hillock, pull a local model, and run your first document query in under 5 minutes.

Architecture

Understand the three-tier design: SQLite Knowledge Graph, Hebbian plasticity, and HDC reservoir.

Python Library

Embed Hillock directly in your Python application using the IntegratedHillock class.

API Reference

Explore the OpenAI-compatible REST API and connect any OpenAI client to Hillock’s memory.

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What makes Hillock different

Zero Hallucinations

An HDC cosine-similarity gate (threshold 0.55) mathematically blocks questions that don’t match stored knowledge. Hillock refuses honestly instead of guessing.

Under 1.2 GB VRAM

The entire pipeline — TALON extractor, HDC reservoir, and Hebbian memory — fits in less than 1.2 GB VRAM. CPU-only mode is supported for truly minimal hardware.

100% Offline

No data ever leaves your machine. Zero cloud dependencies, zero telemetry. Your documents and knowledge graph stay local at all times.

TALON Extraction

The Tensor-Accelerated Local Ontology Network uses O(1) matrix classification to extract Subject-Predicate-Object triples from documents — no LLM pass required for ingestion.

OpenAI-Compatible API

Drop Hillock behind any OpenAI client, Open-WebUI, or AnythingLLM instance. Point your base URL to http://localhost:8000/v1 and chat normally.

Dual-Licensed

Free under AGPL-3.0 for open-source use. Commercial licenses available for proprietary applications — starting at $49/month.